Optimized data rate allocation for dynamic sensor fusion over resource constrained communication networks
Abstract
This article presents a new method to solve a dynamic sensor fusion problem. We consider a large number of remote sensors which measure a common Gauss–Markov process. Each sensor encodes and transmits its measurement to a data fusion center through a resource restricted communication network. The communication cost incurred by a given sensor is quantified as the expected bitrate from the sensor to the fusion center. We propose an approach that attempts to minimize a weighted sum of these communication costs subject to a constraint on the state estimation error at the fusion center. We formulate the problem as a difference‐of‐convex program and apply the convex‐concave procedure (CCP) to obtain a heuristic solution. We consider a 1D heat transfer model and a model for 2D target tracking by a drone swarm for numerical studies. Through these simulations, we observe that our proposed approach has a tendency to assign zero data rate to unnecessary sensors indicating that our approach is sparsity‐promoting, and an effective sensor selection heuristic.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Feb 27, 2022
- Source ID
- 10.1002/rnc.6076
Entities
People
- Ali Reza Pedram
- Hyunho Jung
- Takashi Tanaka
- Travis C. Cuvelier
Organizations
- Defense Advanced Research Projects Agency
- Division of Electrical, Communications & Cyber Systems
- University of Texas at Austin